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          <h1 class="post-title" itemprop="name headline">学术报告</h1>
        

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        <p>学术报告<br><a id="more"></a></p>
<h1 id="基于心电图的心跳分类并检测心律失常"><a href="#基于心电图的心跳分类并检测心律失常" class="headerlink" title="基于心电图的心跳分类并检测心律失常"></a>基于心电图的心跳分类并检测心律失常</h1><h2 id="课题背景"><a href="#课题背景" class="headerlink" title="课题背景"></a>课题背景</h2><p>心电图用于测量每个心跳的电信号，即心脏中不同的专门心脏组织产生的动作脉冲波形的组合，广泛用于检测心脏疾病。存在各种类型的心律不齐，每种类型都与一种电信号模式（通常是频率或者形态上的）相关联，因此通过分析每个心跳的电信号，可以检测其一些异常。但识别和分类心律失常对于医生来说非常麻烦的，因为有时候需要分析很长一段时间内心跳监测器获取的 ECG 记录种的每个心跳，而且人容易犯错误。</p>
<h3 id="心电图简单介绍"><a href="#心电图简单介绍" class="headerlink" title="心电图简单介绍"></a>心电图简单介绍</h3><h4 id="12-导联常规心电图"><a href="#12-导联常规心电图" class="headerlink" title="12 导联常规心电图"></a>12 导联常规心电图</h4><img src="/2017/12/19/学术报告/markdown-img-paste-2017122021061630.png" alt="markdown-img-paste-2017122021061630.png" title="">
<img src="/2017/12/19/学术报告/markdown-img-paste-20171220210701429.png" alt="markdown-img-paste-20171220210701429.png" title="">
<img src="/2017/12/19/学术报告/markdown-img-paste-20171218195730828.png" alt="markdown-img-paste-20171218195730828.png" title="">
<img src="/2017/12/19/学术报告/markdown-img-paste-20171220211513295.png" alt="markdown-img-paste-20171220211513295.png" title="">
<img src="/2017/12/19/学术报告/markdown-img-paste-20171218192705380.png" alt="markdown-img-paste-20171218192705380.png" title="">
<p>P-R 间期：自 P 波起点到 QRS 起点的一段时间，代表窦性激动自心房除极开始到心室除极开始的时间。<br>QRS 波群：代表左右心室包括室间隔除极所产生的电位变化。第 1 个负向波为 Q 波，其后的正向波为 R 波，继 R 波之后的负向波为 S 波。<br>ST 段：心室除极结束至心室开始复极的一段线段。<br>T 波：代表心室复极波。<br>Q-T 间期：心室开始除极至复极结束的全部时间。</p>
<h4 id="动态心电图"><a href="#动态心电图" class="headerlink" title="动态心电图"></a>动态心电图</h4><p>动态心电图是一种可以长时间连续记录并编集分析人体心脏在活动和安静状态下心电图变化的方法。<br>与普通心电图相比，动态心电图于 24 小时内可连续记录多达 10 万次左右的心电信号，这样可以提高对非持续性心律失常，尤其是对一过性心律失常及短暂的心肌缺血发作的检出率，因此扩大了心电图临床运用的范围。<br>目前已成为临床心血管领域中非创伤性检查的重要诊断方法之一。<br><img src="/2017/12/19/学术报告/markdown-img-paste-2017122020343494.png" alt="markdown-img-paste-2017122020343494.png" title=""></p>
<h4 id="心电图的临床应用"><a href="#心电图的临床应用" class="headerlink" title="心电图的临床应用"></a>心电图的临床应用</h4><p><a href="http://www.a-hospital.com/w/%E5%8A%A8%E6%80%81%E5%BF%83%E7%94%B5%E5%9B%BE" target="_blank" rel="external">http://www.a-hospital.com/w/%E5%8A%A8%E6%80%81%E5%BF%83%E7%94%B5%E5%9B%BE</a></p>
<h2 id="课题目的"><a href="#课题目的" class="headerlink" title="课题目的"></a>课题目的</h2><p>分析心电信号并做出异常的检测，需要细致地区分析大量的数据，并且仪器检测到的数据往往含有明显的噪声。这项工作对于大部分医生来讲也是一项耗时长、难以处理、错误率较高的任务。此外，心电图分析具有极高的临床应用价值，已经在全世界广泛应用。</p>
<p>当前已存在大量心电信号异常检测的相关研究，并且部分研究已经取得了较为不错的成果。<br>有关研究：？？？<br><img src="/2017/12/19/学术报告/markdown-img-paste-20171220225524742.png" alt="阅读过的部分文献" title="阅读过的部分文献"><br>存在的问题：依赖公开数据集的研究，由于公开数据集存在类不平衡的问题，决定了其成果不能在新数据上取得满意的成果。使用了私有数据集的研究，由于没有公开数据集，因此他人也就无法复现。<br>这一块有待拓展</p>
<p>因此，开发一套心电图自动分析系统，是社会迫切所需的。此课题的目标是开发一套能实现找出心电信号中可能的异常心跳，辅助医生做诊断的系统。</p>
<h2 id="现有现状"><a href="#现有现状" class="headerlink" title="现有现状"></a>现有现状</h2><p>一个完整的检测心律失常的系统，通常包含以下步骤：</p>
<ol>
<li>ECG 信号预处理；</li>
<li>心跳分割与波的识别；</li>
<li>特征提取；</li>
<li>特征选择与分类；</li>
</ol>
<h3 id="ECG-信号预处理"><a href="#ECG-信号预处理" class="headerlink" title="ECG 信号预处理"></a>ECG 信号预处理</h3><p>目的：其实就是降噪。<br><img src="/2017/12/19/学术报告/markdown-img-paste-2017122015152523.png" alt="markdown-img-paste-2017122015152523.png" title=""><br><img src="/2017/12/19/学术报告/markdown-img-paste-20171220151431387.png" alt="markdown-img-paste-20171220151431387.png" title=""></p>
<p>成熟的方法有：</p>
<ol>
<li>滤波器 – 过滤特定频率的波。</li>
<li>小波转换，分离频域及时域信息。</li>
</ol>
<p>Techniques for preprocessing the ECG signal are widely explored, but the choice of which method to use is intrinsically connected with the final objective of the research. Methods focusing on the heartbeat segmentation from the ECG signal (i.e., detection of the QRS complex, other waves or fiducial points aiming at heartbeat delimitation) tend to require a preprocessing that is different from the methods focusing on the automatic classification of arrhythmias.<br>结果：<br>评价：</p>
<h3 id="心跳分割与波的识别"><a href="#心跳分割与波的识别" class="headerlink" title="心跳分割与波的识别"></a>心跳分割与波的识别</h3><h4 id="心跳分割"><a href="#心跳分割" class="headerlink" title="心跳分割"></a>心跳分割</h4><p>目的：即 R 波峰的识别，找到 R 波峰，就能切分出一个个心跳来。</p>
<p>方法及结果：<br><img src="/2017/12/19/学术报告/markdown-img-paste-20171220151431387.png" alt="markdown-img-paste-20171220151431387.png" title=""><br><img src="/2017/12/19/学术报告/markdown-img-paste-20171218204626791.png" alt="markdown-img-paste-20171218204626791.png" title=""></p>
<p>评价：</p>
<h4 id="波的识别"><a href="#波的识别" class="headerlink" title="波的识别"></a>波的识别</h4><p>目的：识别出各个波的起点、终点、波峰 / 谷。旨在用这些特征描述心跳。<br>方法及结果：<br><img src="/2017/12/19/学术报告/markdown-img-paste-20171218205021394.png" alt="markdown-img-paste-20171218205021394.png" title=""><br><img src="/2017/12/19/学术报告/markdown-img-paste-20171218205339889.png" alt="markdown-img-paste-20171218205339889.png" title=""><br>评价：</p>
<h2 id="学习与预测"><a href="#学习与预测" class="headerlink" title="学习与预测"></a>学习与预测</h2><p>对已知异常类型的单个心跳的心电信号序列做特征建模，用学习到的模型对未知心跳的心电信号序列做分类。</p>
<h3 id="特征提取与特征选择"><a href="#特征提取与特征选择" class="headerlink" title="特征提取与特征选择"></a>特征提取与特征选择</h3><p>The feature extraction stage is the key to the success in the heartbeat classification of the arrhythmia using the ECG signal. Any information extracted from the heartbeat used to discriminate its type maybe considered as a feature. The features can be extracted in various forms directly from the ECG signal ’ s morphology in the time domain and/or in the frequency domain or from the cardiac rhythm. Most popular methods proposed in literature are discussed in Section 5.1. Even though some works regard feature extraction and feature selection as two interchangeable terms, these two process are in fact different. While feature extraction is defined as the stage that involves the description of a heartbeat, feature selection consists in choosing a subset with the most representative features with the objective to improve the classification stage. Section 5.2 is dedicated to describe feature selection approaches.</p>
<h4 id="特征提取"><a href="#特征提取" class="headerlink" title="特征提取"></a>特征提取</h4><p>pass</p>
<h4 id="特征选择"><a href="#特征选择" class="headerlink" title="特征选择"></a>特征选择</h4><p>pass</p>
<h3 id="学习算法"><a href="#学习算法" class="headerlink" title="学习算法"></a>学习算法</h3><p>假设我们前面选择的特征对完成某一种疾病的分类来说是完备的。那现在 models can be built from these data using artificial intelligencealgorithms from machine learning and data mining domains [113 – 115] for arrhythmia heartbeat classification.</p>
<p>常用的方法有 support vector machines (SVM) [40,38,66], artificial neural networks (ANN) [34,116,69] and linear discriminant (LD) [7,37,17], and Reservoir Computing With Logistic Regression (RC) [43].</p>
<h4 id="SVM"><a href="#SVM" class="headerlink" title="SVM"></a>SVM</h4><p>SVM is one of the most popular classifiers found in literature for ECG-based arrhythmia classification methods.</p>
<p>Since SVM presents a negative behavior for imbalanced classes, database balancing techniques for the training phase, which are little explored for this problem, can be studied in future research, as for example, more sophisticated sampling techniques, i.e., Synthetic Minority Over-sampling Technique (SMOTE) [120].</p>
<img src="/2017/12/19/学术报告/markdown-img-paste-2017122020533240.png" alt="markdown-img-paste-2017122020533240.png" title="">
<img src="/2017/12/19/学术报告/markdown-img-paste-20171220205406473.png" alt="markdown-img-paste-20171220205406473.png" title="">
<h4 id="神经网络"><a href="#神经网络" class="headerlink" title="神经网络"></a>神经网络</h4><p><a href="https://stanfordmlgroup.github.io/projects/ecg/" target="_blank" rel="external">https://stanfordmlgroup.github.io/projects/ecg/</a><br><img src="/2017/12/19/学术报告/markdown-img-paste-20171220210113311.png" alt="markdown-img-paste-20171220210113311.png" title=""></p>
<h4 id="Reservoir-computing-with-logistic-regression"><a href="#Reservoir-computing-with-logistic-regression" class="headerlink" title="Reservoir computing with logistic regression"></a>Reservoir computing with logistic regression</h4><p>Reservoir computing is a framework for computation that may be viewed as an extension of neural networks.[1] Typically an input signal is fed into a fixed (random) dynamical system called a reservoir and the dynamics of the reservoir map the input to a higher dimension. Then a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output. The main benefit is that the training is performed only at the readout stage and the reservoir is fixed. Liquid-state machines[2] and echo state networks[3] are two major types of reservoir computing.</p>
<p>Many other methods for arrhythmia classification have been developed using other machine learning and data mining algorithms, such as decision trees [125,126,68], nearest neighbors [127 – 129], clustering [73,130,131], hidden Markov models [132,133], hyperbox classifiers [105], optimum-path forest [134], conditional random fields [8] and rules-based models [135,67,136]. Algorithms with a lazy approach, such as the k Nearest Neighbors (kNN), are not much used for the problem of arrhythmia classification, since their efficiency is intimately connected to previous knowledge to perform the classification of each sample that is represented by the complete training set, which leads to a high computational cost during the testing phase. This cost can invalidate its use for diagnosis in real time. Mishra and Raghav [95] used a classifier based on kNN and reported promising results, however the computational cost was not mentioned. In other works, also based on kNN, in the literature [127,92,128,137,129], no one presented a more fair evaluation protocol for comparison of methods as the one proposed by de Chazal et al. [7], and no one also followed the AAMI recommendations. In addition, the computational cost of these methods was not investigated.</p>
<h3 id="公开数据集"><a href="#公开数据集" class="headerlink" title="公开数据集"></a>公开数据集</h3><p>• MIT-BIH: The Massachusetts Institute of Technology – Beth Israel Hospital Arrhythmia Database (48 records of 30 min each);<br>• EDB: The European Society of Cardiology ST-T Database (90 records of 2 h each);<br>• AHA: The American Heart Association Database for Evaluation of Ventricular Arrhythmia Detectors (80 records of 35 min each);<br>• CU: The Creighton University Sustained Ventricular Arrhythmia Database (35 records of 8 min each);<br>• NST: The Noise Stress Test Database (12 records of ECG of 30 min each, plus 3 records with noise excess);<br>• PTB: The database contains 549 records from 290 subjects (aged 17 to 87, mean 57.2; 209 men, mean age 55.5, and 81 women, mean age 61.6; ages were not recorded for 1 female and 14 male subjects). The diagnostic classes of the remaining 268 subjects are summarized below: Myocardial infarction, Cardiomyopathy/Heart failure, Bundle branch block, Dysrhythmia, Myocardial hypertrophy, Valvular heart disease, Myocarditis, Miscellaneous, Healthy controls</p>
<h3 id="用于性能评估的标准"><a href="#用于性能评估的标准" class="headerlink" title="用于性能评估的标准"></a>用于性能评估的标准</h3><p>This standardization was developed by AAMI and is specified in ANSI/AAMI EC57:1998/(R)2008 [10] and defined the protocol to perform the evaluations to make sure the experiments are reproducible and comparable.</p>
<p>关键在于测试集中的单个心跳的心电信号序列，不能与训练集中的单个心跳的心电信号序列来自同一个病人。如果是来自于同一个病人，会造成测试的准确率高于真实的准确率。</p>
<h2 id="我们的研究条件"><a href="#我们的研究条件" class="headerlink" title="我们的研究条件"></a>我们的研究条件</h2><p>我们有的数据集：<br>可利用的现有成果：</p>
<h2 id="我们的研究思路"><a href="#我们的研究思路" class="headerlink" title="我们的研究思路"></a>我们的研究思路</h2><p>现有的研究存在的问题：</p>
<p>对于简单的疾病，我们模拟医生的诊断方法，对于复杂难以诊断的疾病，我们采用神经网络的方法，并且所需的标注会找医生完成。</p>
<p>我们的研究思路的优点：</p>
<h3 id="模拟医生的诊断方法"><a href="#模拟医生的诊断方法" class="headerlink" title="模拟医生的诊断方法"></a>模拟医生的诊断方法</h3><h4 id="测量参数"><a href="#测量参数" class="headerlink" title="测量参数"></a>测量参数</h4><img src="/2017/12/19/学术报告/markdown-img-paste-20171220212148243.png" alt="markdown-img-paste-20171220212148243.png" title="">
<p>心率：正常心率<br>RR 间期：宽度、形态、频率。<br>QRS 波群：宽度、<br>P 波：方向（直立 / 倒置）；时间 0.05 ～ 0.11s；电压 0.05 ～ 0.24mV；频率 60 ～ 100 次 / 分钟，有学者定义为 50 ～ 90 次 / 分钟。<br>PR 间期（PQ 间期）：正常人在 0.12 ～ 0.20s 之间。<br>QRS 波群：时间 0.06-0.11s；Q， R, S.<br>ST 段：抬高，下降。<br>T 波：方向（直立 / 倒置）。<br>QT 间期：0.32 ～ 0.44s。<br>平均心电轴。</p>
<p>常规心电图基本参数的定义及测量方法<br>心电轴的测量<br>《21 世纪临床心电图教学图谱》</p>
<h4 id="基于心电信号参数的诊断"><a href="#基于心电信号参数的诊断" class="headerlink" title="基于心电信号参数的诊断"></a>基于心电信号参数的诊断</h4><p>– 以识别正常与否为例：<br>【心电图诊断】<br><a href="http://gongjushu.cnki.net/refbook/detail.aspx?db=crfd&amp;recid=R2007041240000011" target="_blank" rel="external">http://gongjushu.cnki.net/refbook/detail.aspx?db=crfd&amp;recid=R2007041240000011</a></p>
<h3 id="聚类"><a href="#聚类" class="headerlink" title="聚类"></a>聚类</h3><p>对单个心跳的心电信号序列聚类<br>聚类之后：</p>
<ol>
<li>我们可以再找医生做标注；</li>
<li>或者根据同一类中各个心跳对应的病人的诊断，从中找出心跳到诊断的对应关系 —- 同一类的所有心跳属于多个病人，这些病人中每个病人都有对应的正确的诊断，那么这些诊断中各个病人共有的诊断就很可能是当前这类心跳对应的诊断。</li>
</ol>
<p>我们也可以用上面的 2. 检验我们的聚类效果怎么样。</p>
<h3 id="语音设别技术"><a href="#语音设别技术" class="headerlink" title="语音设别技术"></a>语音设别技术</h3><p>心跳分类的任务跟语言识别技术很相似，都是对一段时间内的呈波形的信号做分类。</p>
<img src="/2017/12/19/学术报告/webwxgetmsgimg.jpg" alt="webwxgetmsgimg.jpg" title="">
<p>迁移语音识别的神经网络到心跳分类的任务上。</p>
<p>参考文献：<br>[1] E. J. da S. Luz, W. R. Schwartz, G. Cámara-Chávez 和 D. Menotti, 《ECG-based heartbeat classification for arrhythmia detection: A survey》, Computer Methods and Programs in Biomedicine, 卷 127, 期 Supplement C, 页 144 – 164, 2016.<br>[2] 卢喜烈 , 李中健 , 石亚君 . 21 世纪临床心电图教学图谱[M]. 山东科学技术出版社 , 2003.</p>

      
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-1"><a class="nav-link" href="#基于心电图的心跳分类并检测心律失常"><span class="nav-number">1.</span> <span class="nav-text">基于心电图的心跳分类并检测心律失常</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#课题背景"><span class="nav-number">1.1.</span> <span class="nav-text">课题背景</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#心电图简单介绍"><span class="nav-number">1.1.1.</span> <span class="nav-text">心电图简单介绍</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#12-导联常规心电图"><span class="nav-number">1.1.1.1.</span> <span class="nav-text">12 导联常规心电图</span></a></li><li class="nav-item nav-level-4"><a class="nav-link" href="#动态心电图"><span class="nav-number">1.1.1.2.</span> <span class="nav-text">动态心电图</span></a></li><li class="nav-item nav-level-4"><a class="nav-link" href="#心电图的临床应用"><span class="nav-number">1.1.1.3.</span> <span class="nav-text">心电图的临床应用</span></a></li></ol></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#课题目的"><span class="nav-number">1.2.</span> <span class="nav-text">课题目的</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#现有现状"><span class="nav-number">1.3.</span> <span class="nav-text">现有现状</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#ECG-信号预处理"><span class="nav-number">1.3.1.</span> <span class="nav-text">ECG 信号预处理</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#心跳分割与波的识别"><span class="nav-number">1.3.2.</span> <span class="nav-text">心跳分割与波的识别</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#心跳分割"><span class="nav-number">1.3.2.1.</span> <span class="nav-text">心跳分割</span></a></li><li class="nav-item nav-level-4"><a class="nav-link" href="#波的识别"><span class="nav-number">1.3.2.2.</span> <span class="nav-text">波的识别</span></a></li></ol></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#学习与预测"><span class="nav-number">1.4.</span> <span class="nav-text">学习与预测</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#特征提取与特征选择"><span class="nav-number">1.4.1.</span> <span class="nav-text">特征提取与特征选择</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#特征提取"><span class="nav-number">1.4.1.1.</span> <span class="nav-text">特征提取</span></a></li><li class="nav-item nav-level-4"><a class="nav-link" href="#特征选择"><span class="nav-number">1.4.1.2.</span> <span class="nav-text">特征选择</span></a></li></ol></li><li class="nav-item nav-level-3"><a class="nav-link" href="#学习算法"><span class="nav-number">1.4.2.</span> <span class="nav-text">学习算法</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#SVM"><span class="nav-number">1.4.2.1.</span> <span class="nav-text">SVM</span></a></li><li class="nav-item nav-level-4"><a class="nav-link" href="#神经网络"><span class="nav-number">1.4.2.2.</span> <span class="nav-text">神经网络</span></a></li><li class="nav-item nav-level-4"><a class="nav-link" href="#Reservoir-computing-with-logistic-regression"><span class="nav-number">1.4.2.3.</span> <span class="nav-text">Reservoir computing with logistic regression</span></a></li></ol></li><li class="nav-item nav-level-3"><a class="nav-link" href="#公开数据集"><span class="nav-number">1.4.3.</span> <span class="nav-text">公开数据集</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#用于性能评估的标准"><span class="nav-number">1.4.4.</span> <span class="nav-text">用于性能评估的标准</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#我们的研究条件"><span class="nav-number">1.5.</span> <span class="nav-text">我们的研究条件</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#我们的研究思路"><span class="nav-number">1.6.</span> <span class="nav-text">我们的研究思路</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#模拟医生的诊断方法"><span class="nav-number">1.6.1.</span> <span class="nav-text">模拟医生的诊断方法</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#测量参数"><span class="nav-number">1.6.1.1.</span> <span class="nav-text">测量参数</span></a></li><li class="nav-item nav-level-4"><a class="nav-link" href="#基于心电信号参数的诊断"><span class="nav-number">1.6.1.2.</span> <span class="nav-text">基于心电信号参数的诊断</span></a></li></ol></li><li class="nav-item nav-level-3"><a class="nav-link" href="#聚类"><span class="nav-number">1.6.2.</span> <span class="nav-text">聚类</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#语音设别技术"><span class="nav-number">1.6.3.</span> <span class="nav-text">语音设别技术</span></a></li></ol></li></ol></li></ol></div>
            

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                    function getIndexByWord(word, text, caseSensitive) {
                      var wordLen = word.length;
                      if (wordLen === 0) {
                        return [];
                      }
                      var startPosition = 0, position = [], index = [];
                      if (!caseSensitive) {
                        text = text.toLowerCase();
                        word = word.toLowerCase();
                      }
                      while ((position = text.indexOf(word, startPosition)) > -1) {
                        index.push({position: position, word: word});
                        startPosition = position + wordLen;
                      }
                      return index;
                    }

                    indexOfTitle = indexOfTitle.concat(getIndexByWord(keyword, titleInLowerCase, false));
                    indexOfContent = indexOfContent.concat(getIndexByWord(keyword, contentInLowerCase, false));
                  });
                  if (indexOfTitle.length > 0 || indexOfContent.length > 0) {
                    isMatch = true;
                    hitCount = indexOfTitle.length + indexOfContent.length;
                  }
                }

                // show search results

                if (isMatch) {
                  // sort index by position of keyword

                  [indexOfTitle, indexOfContent].forEach(function (index) {
                    index.sort(function (itemLeft, itemRight) {
                      if (itemRight.position !== itemLeft.position) {
                        return itemRight.position - itemLeft.position;
                      } else {
                        return itemLeft.word.length - itemRight.word.length;
                      }
                    });
                  });

                  // merge hits into slices

                  function mergeIntoSlice(text, start, end, index) {
                    var item = index[index.length - 1];
                    var position = item.position;
                    var word = item.word;
                    var hits = [];
                    var searchTextCountInSlice = 0;
                    while (position + word.length <= end && index.length != 0) {
                      if (word === searchText) {
                        searchTextCountInSlice++;
                      }
                      hits.push({position: position, length: word.length});
                      var wordEnd = position + word.length;

                      // move to next position of hit

                      index.pop();
                      while (index.length != 0) {
                        item = index[index.length - 1];
                        position = item.position;
                        word = item.word;
                        if (wordEnd > position) {
                          index.pop();
                        } else {
                          break;
                        }
                      }
                    }
                    searchTextCount += searchTextCountInSlice;
                    return {
                      hits: hits,
                      start: start,
                      end: end,
                      searchTextCount: searchTextCountInSlice
                    };
                  }

                  var slicesOfTitle = [];
                  if (indexOfTitle.length != 0) {
                    slicesOfTitle.push(mergeIntoSlice(title, 0, title.length, indexOfTitle));
                  }

                  var slicesOfContent = [];
                  while (indexOfContent.length != 0) {
                    var item = indexOfContent[indexOfContent.length - 1];
                    var position = item.position;
                    var word = item.word;
                    // cut out 100 characters
                    var start = position - 20;
                    var end = position + 80;
                    if(start < 0){
                      start = 0;
                    }
                    if (end < position + word.length) {
                      end = position + word.length;
                    }
                    if(end > content.length){
                      end = content.length;
                    }
                    slicesOfContent.push(mergeIntoSlice(content, start, end, indexOfContent));
                  }

                  // sort slices in content by search text's count and hits' count

                  slicesOfContent.sort(function (sliceLeft, sliceRight) {
                    if (sliceLeft.searchTextCount !== sliceRight.searchTextCount) {
                      return sliceRight.searchTextCount - sliceLeft.searchTextCount;
                    } else if (sliceLeft.hits.length !== sliceRight.hits.length) {
                      return sliceRight.hits.length - sliceLeft.hits.length;
                    } else {
                      return sliceLeft.start - sliceRight.start;
                    }
                  });

                  // select top N slices in content

                  var upperBound = parseInt('1');
                  if (upperBound >= 0) {
                    slicesOfContent = slicesOfContent.slice(0, upperBound);
                  }

                  // highlight title and content

                  function highlightKeyword(text, slice) {
                    var result = '';
                    var prevEnd = slice.start;
                    slice.hits.forEach(function (hit) {
                      result += text.substring(prevEnd, hit.position);
                      var end = hit.position + hit.length;
                      result += '<b class="search-keyword">' + text.substring(hit.position, end) + '</b>';
                      prevEnd = end;
                    });
                    result += text.substring(prevEnd, slice.end);
                    return result;
                  }

                  var resultItem = '';

                  if (slicesOfTitle.length != 0) {
                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + highlightKeyword(title, slicesOfTitle[0]) + "</a>";
                  } else {
                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + title + "</a>";
                  }

                  slicesOfContent.forEach(function (slice) {
                    resultItem += "<a href='" + articleUrl + "'>" +
                      "<p class=\"search-result\">" + highlightKeyword(content, slice) +
                      "...</p>" + "</a>";
                  });

                  resultItem += "</li>";
                  resultItems.push({
                    item: resultItem,
                    searchTextCount: searchTextCount,
                    hitCount: hitCount,
                    id: resultItems.length
                  });
                }
              })
            };
            if (keywords.length === 1 && keywords[0] === "") {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-search fa-5x" /></div>'
            } else if (resultItems.length === 0) {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-frown-o fa-5x" /></div>'
            } else {
              resultItems.sort(function (resultLeft, resultRight) {
                if (resultLeft.searchTextCount !== resultRight.searchTextCount) {
                  return resultRight.searchTextCount - resultLeft.searchTextCount;
                } else if (resultLeft.hitCount !== resultRight.hitCount) {
                  return resultRight.hitCount - resultLeft.hitCount;
                } else {
                  return resultRight.id - resultLeft.id;
                }
              });
              var searchResultList = '<ul class=\"search-result-list\">';
              resultItems.forEach(function (result) {
                searchResultList += result.item;
              })
              searchResultList += "</ul>";
              resultContent.innerHTML = searchResultList;
            }
          }

          if ('auto' === 'manual') {
            input.addEventListener('input', inputEventFunction);
          } else {
            $('.search-icon').click(inputEventFunction);
            input.addEventListener('keypress', function (event) {
              if (event.keyCode === 13) {
                inputEventFunction();
              }
            });
          }

          // remove loading animation
          $(".local-search-pop-overlay").remove();
          $('body').css('overflow', '');

          proceedsearch();
        }
      });
    }

    // handle and trigger popup window;
    $('.popup-trigger').click(function(e) {
      e.stopPropagation();
      if (isfetched === false) {
        searchFunc(path, 'local-search-input', 'local-search-result');
      } else {
        proceedsearch();
      };
    });

    $('.popup-btn-close').click(onPopupClose);
    $('.popup').click(function(e){
      e.stopPropagation();
    });
    $(document).on('keyup', function (event) {
      var shouldDismissSearchPopup = event.which === 27 &&
        $('.search-popup').is(':visible');
      if (shouldDismissSearchPopup) {
        onPopupClose();
      }
    });
  </script>





  

  

  

  
  
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